Overview

Dataset statistics

Number of variables28
Number of observations75166
Missing cells82295
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 MiB
Average record size in memory224.0 B

Variable types

Numeric13
Categorical15

Alerts

country has a high cardinality: 165 distinct values High cardinality
agent has 12310 (16.4%) missing values Missing
company has 69560 (92.5%) missing values Missing
previous_cancellations is highly skewed (γ1 = 29.14223836) Skewed
df_index has unique values Unique
lead_time has 5915 (7.9%) zeros Zeros
previous_cancellations has 74624 (99.3%) zeros Zeros
previous_bookings_not_canceled has 71746 (95.5%) zeros Zeros
booking_changes has 59920 (79.7%) zeros Zeros
days_in_waiting_list has 73827 (98.2%) zeros Zeros
total_of_special_requests has 36762 (48.9%) zeros Zeros

Reproduction

Analysis started2023-02-05 02:48:23.103735
Analysis finished2023-02-05 02:49:40.814839
Duration1 minute and 17.71 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct75166
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66098.29473
Minimum0
Maximum119389
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:40.995819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13895.25
Q129905.25
median80541.5
Q3100580.75
95-th percentile115629.75
Maximum119389
Range119389
Interquartile range (IQR)70675.5

Descriptive statistics

Standard deviation37543.7521
Coefficient of variation (CV)0.5679988002
Kurtosis-1.566288398
Mean66098.29473
Median Absolute Deviation (MAD)34285.5
Skewness-0.1500540579
Sum4968344422
Variance1409533322
MonotonicityStrictly increasing
2023-02-04T21:49:41.211322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
942951
 
< 0.1%
943121
 
< 0.1%
943111
 
< 0.1%
943101
 
< 0.1%
943091
 
< 0.1%
943071
 
< 0.1%
943061
 
< 0.1%
943051
 
< 0.1%
943041
 
< 0.1%
Other values (75156)75156
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
1193891
< 0.1%
1193881
< 0.1%
1193871
< 0.1%
1193861
< 0.1%
1193851
< 0.1%
1193841
< 0.1%
1193831
< 0.1%
1193821
< 0.1%
1193811
< 0.1%
1193801
< 0.1%

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
City Hotel
46228 
Resort Hotel
28938 

Length

Max length12
Median length10
Mean length10.76997579
Min length10

Characters and Unicode

Total characters809536
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel46228
61.5%
Resort Hotel28938
38.5%

Length

2023-02-04T21:49:41.402314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:41.583120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
hotel75166
50.0%
city46228
30.8%
resort28938
 
19.2%

Most occurring characters

ValueCountFrequency (%)
t150332
18.6%
o104104
12.9%
e104104
12.9%
75166
9.3%
H75166
9.3%
l75166
9.3%
C46228
 
5.7%
i46228
 
5.7%
y46228
 
5.7%
R28938
 
3.6%
Other values (2)57876
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter584038
72.1%
Uppercase Letter150332
 
18.6%
Space Separator75166
 
9.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t150332
25.7%
o104104
17.8%
e104104
17.8%
l75166
12.9%
i46228
 
7.9%
y46228
 
7.9%
s28938
 
5.0%
r28938
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
H75166
50.0%
C46228
30.8%
R28938
 
19.2%
Space Separator
ValueCountFrequency (%)
75166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin734370
90.7%
Common75166
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t150332
20.5%
o104104
14.2%
e104104
14.2%
H75166
10.2%
l75166
10.2%
C46228
 
6.3%
i46228
 
6.3%
y46228
 
6.3%
R28938
 
3.9%
s28938
 
3.9%
Common
ValueCountFrequency (%)
75166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII809536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t150332
18.6%
o104104
12.9%
e104104
12.9%
75166
9.3%
H75166
9.3%
l75166
9.3%
C46228
 
5.7%
i46228
 
5.7%
y46228
 
5.7%
R28938
 
3.6%
Other values (2)57876
 
7.1%

lead_time
Real number (ℝ≥0)

ZEROS

Distinct422
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.98468723
Minimum0
Maximum737
Zeros5915
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:41.740117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median45
Q3124
95-th percentile269
Maximum737
Range737
Interquartile range (IQR)115

Descriptive statistics

Standard deviation91.10988782
Coefficient of variation (CV)1.139091631
Kurtosis2.292428857
Mean79.98468723
Median Absolute Deviation (MAD)42
Skewness1.526687435
Sum6012129
Variance8301.011659
MonotonicityNot monotonic
2023-02-04T21:49:41.933463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05915
 
7.9%
13139
 
4.2%
21856
 
2.5%
31634
 
2.2%
41539
 
2.0%
51359
 
1.8%
61243
 
1.7%
71159
 
1.5%
8915
 
1.2%
11834
 
1.1%
Other values (412)55573
73.9%
ValueCountFrequency (%)
05915
7.9%
13139
4.2%
21856
 
2.5%
31634
 
2.2%
41539
 
2.0%
51359
 
1.8%
61243
 
1.7%
71159
 
1.5%
8915
 
1.2%
9773
 
1.0%
ValueCountFrequency (%)
7371
 
< 0.1%
7091
 
< 0.1%
54223
< 0.1%
5321
 
< 0.1%
51822
< 0.1%
50421
< 0.1%
47920
< 0.1%
4782
 
< 0.1%
47615
< 0.1%
46830
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
2016
36370 
2017
24942 
2015
13854 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters300664
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
201636370
48.4%
201724942
33.2%
201513854
 
18.4%

Length

2023-02-04T21:49:42.104677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:42.276919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
201636370
48.4%
201724942
33.2%
201513854
 
18.4%

Most occurring characters

ValueCountFrequency (%)
275166
25.0%
075166
25.0%
175166
25.0%
636370
12.1%
724942
 
8.3%
513854
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number300664
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
275166
25.0%
075166
25.0%
175166
25.0%
636370
12.1%
724942
 
8.3%
513854
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common300664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
275166
25.0%
075166
25.0%
175166
25.0%
636370
12.1%
724942
 
8.3%
513854
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII300664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
275166
25.0%
075166
25.0%
175166
25.0%
636370
12.1%
724942
 
8.3%
513854
 
4.6%

arrival_date_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.526355001
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:42.408633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.153542756
Coefficient of variation (CV)0.4832012288
Kurtosis-1.042706178
Mean6.526355001
Median Absolute Deviation (MAD)3
Skewness-0.02586837604
Sum490560
Variance9.944831913
MonotonicityNot monotonic
2023-02-04T21:49:42.544077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
88638
11.5%
77919
10.5%
57114
9.5%
106914
9.2%
36645
8.8%
46565
8.7%
66404
8.5%
96392
8.5%
25372
7.1%
114672
6.2%
Other values (2)8531
11.3%
ValueCountFrequency (%)
14122
5.5%
25372
7.1%
36645
8.8%
46565
8.7%
57114
9.5%
66404
8.5%
77919
10.5%
88638
11.5%
96392
8.5%
106914
9.2%
ValueCountFrequency (%)
124409
5.9%
114672
6.2%
106914
9.2%
96392
8.5%
88638
11.5%
77919
10.5%
66404
8.5%
57114
9.5%
46565
8.7%
36645
8.8%

arrival_date_week_number
Real number (ℝ≥0)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.08014262
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:42.716346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.90247758
Coefficient of variation (CV)0.5133827311
Kurtosis-1.034346993
Mean27.08014262
Median Absolute Deviation (MAD)11
Skewness-0.008567424333
Sum2035506
Variance193.2788827
MonotonicityNot monotonic
2023-02-04T21:49:42.941736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
332075
 
2.8%
341996
 
2.7%
321879
 
2.5%
301844
 
2.5%
281804
 
2.4%
211794
 
2.4%
291747
 
2.3%
311746
 
2.3%
421681
 
2.2%
271679
 
2.2%
Other values (43)56921
75.7%
ValueCountFrequency (%)
1694
0.9%
2823
1.1%
3984
1.3%
4981
1.3%
5962
1.3%
61123
1.5%
71425
1.9%
81423
1.9%
91393
1.9%
101412
1.9%
ValueCountFrequency (%)
531172
1.6%
52841
1.1%
51687
0.9%
50908
1.2%
491117
1.5%
481129
1.5%
471309
1.7%
46985
1.3%
451180
1.6%
441527
2.0%

arrival_date_day_of_month
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.83952851
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:43.159128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.776422332
Coefficient of variation (CV)0.5540835591
Kurtosis-1.186816098
Mean15.83952851
Median Absolute Deviation (MAD)8
Skewness-0.007459004258
Sum1190594
Variance77.02558894
MonotonicityNot monotonic
2023-02-04T21:49:43.354782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
252680
 
3.6%
22670
 
3.6%
182642
 
3.5%
52638
 
3.5%
192583
 
3.4%
172569
 
3.4%
62556
 
3.4%
202549
 
3.4%
92546
 
3.4%
122541
 
3.4%
Other values (21)49192
65.4%
ValueCountFrequency (%)
12182
2.9%
22670
3.6%
32310
3.1%
42412
3.2%
52638
3.5%
62556
3.4%
72224
3.0%
82274
3.0%
92546
3.4%
102389
3.2%
ValueCountFrequency (%)
311448
1.9%
302390
3.2%
292318
3.1%
282435
3.2%
272387
3.2%
262521
3.4%
252680
3.6%
242494
3.3%
232440
3.2%
222255
3.0%

adults
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
2
54422 
1
16353 
3
 
4051
0
 
294
4
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75166
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
254422
72.4%
116353
 
21.8%
34051
 
5.4%
0294
 
0.4%
446
 
0.1%

Length

2023-02-04T21:49:43.566165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:43.770255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
254422
72.4%
116353
 
21.8%
34051
 
5.4%
0294
 
0.4%
446
 
0.1%

Most occurring characters

ValueCountFrequency (%)
254422
72.4%
116353
 
21.8%
34051
 
5.4%
0294
 
0.4%
446
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75166
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
254422
72.4%
116353
 
21.8%
34051
 
5.4%
0294
 
0.4%
446
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common75166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
254422
72.4%
116353
 
21.8%
34051
 
5.4%
0294
 
0.4%
446
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII75166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
254422
72.4%
116353
 
21.8%
34051
 
5.4%
0294
 
0.4%
446
 
0.1%

children
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
0.0
69702 
1.0
 
3294
2.0
 
2111
3.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters225498
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.069702
92.7%
1.03294
 
4.4%
2.02111
 
2.8%
3.059
 
0.1%

Length

2023-02-04T21:49:43.940623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:44.134313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.069702
92.7%
1.03294
 
4.4%
2.02111
 
2.8%
3.059
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0144868
64.2%
.75166
33.3%
13294
 
1.5%
22111
 
0.9%
359
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150332
66.7%
Other Punctuation75166
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0144868
96.4%
13294
 
2.2%
22111
 
1.4%
359
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.75166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common225498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0144868
64.2%
.75166
33.3%
13294
 
1.5%
22111
 
0.9%
359
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII225498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0144868
64.2%
.75166
33.3%
13294
 
1.5%
22111
 
0.9%
359
 
< 0.1%

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
0
74416 
1
 
735
2
 
13
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.000013304
Min length1

Characters and Unicode

Total characters75167
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
074416
99.0%
1735
 
1.0%
213
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Length

2023-02-04T21:49:44.306650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:44.507096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
074416
99.0%
1735
 
1.0%
213
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
074417
99.0%
1736
 
1.0%
213
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75167
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
074417
99.0%
1736
 
1.0%
213
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common75167
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
074417
99.0%
1736
 
1.0%
213
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII75167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
074417
99.0%
1736
 
1.0%
213
 
< 0.1%
91
 
< 0.1%

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
BB
57800 
HB
9479 
SC
6684 
Undefined
 
883
FB
 
320

Length

Max length9
Median length2
Mean length2.082231328
Min length2

Characters and Unicode

Total characters156513
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB57800
76.9%
HB9479
 
12.6%
SC6684
 
8.9%
Undefined883
 
1.2%
FB320
 
0.4%

Length

2023-02-04T21:49:44.687431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:44.913159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
bb57800
76.9%
hb9479
 
12.6%
sc6684
 
8.9%
undefined883
 
1.2%
fb320
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B125399
80.1%
H9479
 
6.1%
S6684
 
4.3%
C6684
 
4.3%
n1766
 
1.1%
d1766
 
1.1%
e1766
 
1.1%
U883
 
0.6%
f883
 
0.6%
i883
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter149449
95.5%
Lowercase Letter7064
 
4.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B125399
83.9%
H9479
 
6.3%
S6684
 
4.5%
C6684
 
4.5%
U883
 
0.6%
F320
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
n1766
25.0%
d1766
25.0%
e1766
25.0%
f883
12.5%
i883
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin156513
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B125399
80.1%
H9479
 
6.1%
S6684
 
4.3%
C6684
 
4.3%
n1766
 
1.1%
d1766
 
1.1%
e1766
 
1.1%
U883
 
0.6%
f883
 
0.6%
i883
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII156513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B125399
80.1%
H9479
 
6.1%
S6684
 
4.3%
C6684
 
4.3%
n1766
 
1.1%
d1766
 
1.1%
e1766
 
1.1%
U883
 
0.6%
f883
 
0.6%
i883
 
0.6%

country
Categorical

HIGH CARDINALITY

Distinct165
Distinct (%)0.2%
Missing421
Missing (%)0.6%
Memory size587.4 KiB
PRT
21071 
GBR
9676 
FRA
8481 
ESP
6391 
DEU
6069 
Other values (160)
23057 

Length

Max length3
Median length3
Mean length2.986286708
Min length2

Characters and Unicode

Total characters223210
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR

Common Values

ValueCountFrequency (%)
PRT21071
28.0%
GBR9676
12.9%
FRA8481
11.3%
ESP6391
 
8.5%
DEU6069
 
8.1%
IRL2543
 
3.4%
ITA2433
 
3.2%
BEL1868
 
2.5%
NLD1717
 
2.3%
USA1596
 
2.1%
Other values (155)12900
17.2%

Length

2023-02-04T21:49:45.136226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt21071
28.2%
gbr9676
12.9%
fra8481
11.3%
esp6391
 
8.6%
deu6069
 
8.1%
irl2543
 
3.4%
ita2433
 
3.3%
bel1868
 
2.5%
nld1717
 
2.3%
usa1596
 
2.1%
Other values (155)12900
17.3%

Most occurring characters

ValueCountFrequency (%)
R45992
20.6%
P28483
12.8%
T24966
11.2%
E16818
 
7.5%
A16083
 
7.2%
B13222
 
5.9%
U10476
 
4.7%
S10385
 
4.7%
G10248
 
4.6%
F8917
 
4.0%
Other values (16)37620
16.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter223210
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R45992
20.6%
P28483
12.8%
T24966
11.2%
E16818
 
7.5%
A16083
 
7.2%
B13222
 
5.9%
U10476
 
4.7%
S10385
 
4.7%
G10248
 
4.6%
F8917
 
4.0%
Other values (16)37620
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin223210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R45992
20.6%
P28483
12.8%
T24966
11.2%
E16818
 
7.5%
A16083
 
7.2%
B13222
 
5.9%
U10476
 
4.7%
S10385
 
4.7%
G10248
 
4.6%
F8917
 
4.0%
Other values (16)37620
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII223210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R45992
20.6%
P28483
12.8%
T24966
11.2%
E16818
 
7.5%
A16083
 
7.2%
B13222
 
5.9%
U10476
 
4.7%
S10385
 
4.7%
G10248
 
4.6%
F8917
 
4.0%
Other values (16)37620
16.9%

market_segment
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
Online TA
35738 
Offline TA/TO
15908 
Direct
10672 
Groups
7714 
Corporate
4303 
Other values (2)
 
831

Length

Max length13
Median length9
Mean length9.144653168
Min length6

Characters and Unicode

Total characters687367
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA35738
47.5%
Offline TA/TO15908
21.2%
Direct10672
 
14.2%
Groups7714
 
10.3%
Corporate4303
 
5.7%
Complementary646
 
0.9%
Aviation185
 
0.2%

Length

2023-02-04T21:49:45.348879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:45.604037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
online35738
28.2%
ta35738
28.2%
offline15908
12.5%
ta/to15908
12.5%
direct10672
 
8.4%
groups7714
 
6.1%
corporate4303
 
3.4%
complementary646
 
0.5%
aviation185
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n88215
12.8%
e67913
9.9%
O67554
9.8%
T67554
9.8%
i62688
9.1%
l52292
7.6%
A51831
7.5%
51646
7.5%
f31816
 
4.6%
r27638
 
4.0%
Other values (14)118220
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter409539
59.6%
Uppercase Letter210274
30.6%
Space Separator51646
 
7.5%
Other Punctuation15908
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n88215
21.5%
e67913
16.6%
i62688
15.3%
l52292
12.8%
f31816
 
7.8%
r27638
 
6.7%
o17151
 
4.2%
t15806
 
3.9%
p12663
 
3.1%
c10672
 
2.6%
Other values (6)22685
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
O67554
32.1%
T67554
32.1%
A51831
24.6%
D10672
 
5.1%
G7714
 
3.7%
C4949
 
2.4%
Space Separator
ValueCountFrequency (%)
51646
100.0%
Other Punctuation
ValueCountFrequency (%)
/15908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin619813
90.2%
Common67554
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n88215
14.2%
e67913
11.0%
O67554
10.9%
T67554
10.9%
i62688
10.1%
l52292
8.4%
A51831
8.4%
f31816
 
5.1%
r27638
 
4.5%
o17151
 
2.8%
Other values (12)85161
13.7%
Common
ValueCountFrequency (%)
51646
76.5%
/15908
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII687367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n88215
12.8%
e67913
9.9%
O67554
9.8%
T67554
9.8%
i62688
9.1%
l52292
7.6%
A51831
7.5%
51646
7.5%
f31816
 
4.6%
r27638
 
4.0%
Other values (14)118220
17.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
TA/TO
57718 
Direct
12088 
Corporate
 
5203
GDS
 
156
Undefined
 
1

Length

Max length9
Median length5
Mean length5.433600298
Min length3

Characters and Unicode

Total characters408422
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO57718
76.8%
Direct12088
 
16.1%
Corporate5203
 
6.9%
GDS156
 
0.2%
Undefined1
 
< 0.1%

Length

2023-02-04T21:49:45.835878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:46.072871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ta/to57718
76.8%
direct12088
 
16.1%
corporate5203
 
6.9%
gds156
 
0.2%
undefined1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T115436
28.3%
/57718
14.1%
O57718
14.1%
A57718
14.1%
r22494
 
5.5%
e17293
 
4.2%
t17291
 
4.2%
D12244
 
3.0%
i12089
 
3.0%
c12088
 
3.0%
Other values (10)26333
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter248632
60.9%
Lowercase Letter102072
25.0%
Other Punctuation57718
 
14.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r22494
22.0%
e17293
16.9%
t17291
16.9%
i12089
11.8%
c12088
11.8%
o10406
10.2%
a5203
 
5.1%
p5203
 
5.1%
n2
 
< 0.1%
d2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T115436
46.4%
O57718
23.2%
A57718
23.2%
D12244
 
4.9%
C5203
 
2.1%
G156
 
0.1%
S156
 
0.1%
U1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/57718
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin350704
85.9%
Common57718
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T115436
32.9%
O57718
16.5%
A57718
16.5%
r22494
 
6.4%
e17293
 
4.9%
t17291
 
4.9%
D12244
 
3.5%
i12089
 
3.4%
c12088
 
3.4%
o10406
 
3.0%
Other values (9)15927
 
4.5%
Common
ValueCountFrequency (%)
/57718
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII408422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T115436
28.3%
/57718
14.1%
O57718
14.1%
A57718
14.1%
r22494
 
5.5%
e17293
 
4.2%
t17291
 
4.2%
D12244
 
3.0%
i12089
 
3.0%
c12088
 
3.0%
Other values (10)26333
 
6.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
0
71908 
1
 
3258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75166
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
071908
95.7%
13258
 
4.3%

Length

2023-02-04T21:49:46.235381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:46.391924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
071908
95.7%
13258
 
4.3%

Most occurring characters

ValueCountFrequency (%)
071908
95.7%
13258
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75166
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
071908
95.7%
13258
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common75166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
071908
95.7%
13258
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
071908
95.7%
13258
 
4.3%

previous_cancellations
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01579171434
Minimum0
Maximum13
Zeros74624
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:46.500277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2724214875
Coefficient of variation (CV)17.25091283
Kurtosis1043.553303
Mean0.01579171434
Median Absolute Deviation (MAD)0
Skewness29.14223836
Sum1187
Variance0.07421346684
MonotonicityNot monotonic
2023-02-04T21:49:46.650426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
074624
99.3%
1337
 
0.4%
278
 
0.1%
345
 
0.1%
1125
 
< 0.1%
424
 
< 0.1%
517
 
< 0.1%
615
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
074624
99.3%
1337
 
0.4%
278
 
0.1%
345
 
0.1%
424
 
< 0.1%
517
 
< 0.1%
615
 
< 0.1%
1125
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
1125
 
< 0.1%
615
 
< 0.1%
517
 
< 0.1%
424
 
< 0.1%
345
 
0.1%
278
 
0.1%
1337
 
0.4%
074624
99.3%

previous_bookings_not_canceled
Real number (ℝ≥0)

ZEROS

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20297741
Minimum0
Maximum72
Zeros71746
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:46.837038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.81071327
Coefficient of variation (CV)8.920762514
Kurtosis534.2063652
Mean0.20297741
Median Absolute Deviation (MAD)0
Skewness19.60351104
Sum15257
Variance3.278682547
MonotonicityNot monotonic
2023-02-04T21:49:47.036609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071746
95.5%
11463
 
1.9%
2548
 
0.7%
3316
 
0.4%
4217
 
0.3%
5170
 
0.2%
6112
 
0.1%
783
 
0.1%
867
 
0.1%
960
 
0.1%
Other values (63)384
 
0.5%
ValueCountFrequency (%)
071746
95.5%
11463
 
1.9%
2548
 
0.7%
3316
 
0.4%
4217
 
0.3%
5170
 
0.2%
6112
 
0.1%
783
 
0.1%
867
 
0.1%
960
 
0.1%
ValueCountFrequency (%)
721
< 0.1%
711
< 0.1%
701
< 0.1%
691
< 0.1%
681
< 0.1%
671
< 0.1%
661
< 0.1%
651
< 0.1%
641
< 0.1%
631
< 0.1%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
A
52364 
D
13099 
E
 
4621
F
 
2017
G
 
1331
Other values (4)
 
1734

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75166
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A52364
69.7%
D13099
 
17.4%
E4621
 
6.1%
F2017
 
2.7%
G1331
 
1.8%
B750
 
1.0%
C624
 
0.8%
H356
 
0.5%
L4
 
< 0.1%

Length

2023-02-04T21:49:47.224092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:47.402650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
a52364
69.7%
d13099
 
17.4%
e4621
 
6.1%
f2017
 
2.7%
g1331
 
1.8%
b750
 
1.0%
c624
 
0.8%
h356
 
0.5%
l4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A52364
69.7%
D13099
 
17.4%
E4621
 
6.1%
F2017
 
2.7%
G1331
 
1.8%
B750
 
1.0%
C624
 
0.8%
H356
 
0.5%
L4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter75166
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A52364
69.7%
D13099
 
17.4%
E4621
 
6.1%
F2017
 
2.7%
G1331
 
1.8%
B750
 
1.0%
C624
 
0.8%
H356
 
0.5%
L4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin75166
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A52364
69.7%
D13099
 
17.4%
E4621
 
6.1%
F2017
 
2.7%
G1331
 
1.8%
B750
 
1.0%
C624
 
0.8%
H356
 
0.5%
L4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII75166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A52364
69.7%
D13099
 
17.4%
E4621
 
6.1%
F2017
 
2.7%
G1331
 
1.8%
B750
 
1.0%
C624
 
0.8%
H356
 
0.5%
L4
 
< 0.1%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
A
41105 
D
18960 
E
5838 
F
 
2824
C
 
1929
Other values (5)
4510 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75166
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A41105
54.7%
D18960
25.2%
E5838
 
7.8%
F2824
 
3.8%
C1929
 
2.6%
G1773
 
2.4%
B1651
 
2.2%
H461
 
0.6%
I358
 
0.5%
K267
 
0.4%

Length

2023-02-04T21:49:47.580471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:47.772528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
a41105
54.7%
d18960
25.2%
e5838
 
7.8%
f2824
 
3.8%
c1929
 
2.6%
g1773
 
2.4%
b1651
 
2.2%
h461
 
0.6%
i358
 
0.5%
k267
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A41105
54.7%
D18960
25.2%
E5838
 
7.8%
F2824
 
3.8%
C1929
 
2.6%
G1773
 
2.4%
B1651
 
2.2%
H461
 
0.6%
I358
 
0.5%
K267
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter75166
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A41105
54.7%
D18960
25.2%
E5838
 
7.8%
F2824
 
3.8%
C1929
 
2.6%
G1773
 
2.4%
B1651
 
2.2%
H461
 
0.6%
I358
 
0.5%
K267
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin75166
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A41105
54.7%
D18960
25.2%
E5838
 
7.8%
F2824
 
3.8%
C1929
 
2.6%
G1773
 
2.4%
B1651
 
2.2%
H461
 
0.6%
I358
 
0.5%
K267
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII75166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A41105
54.7%
D18960
25.2%
E5838
 
7.8%
F2824
 
3.8%
C1929
 
2.6%
G1773
 
2.4%
B1651
 
2.2%
H461
 
0.6%
I358
 
0.5%
K267
 
0.4%

booking_changes
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.2933663287
Minimum0
Maximum21
Zeros59920
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:47.943717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7362788719
Coefficient of variation (CV)2.5097593
Kurtosis66.61474151
Mean0.2933663287
Median Absolute Deviation (MAD)0
Skewness5.397378632
Sum22050
Variance0.5421065772
MonotonicityNot monotonic
2023-02-04T21:49:48.090437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
059920
79.7%
110893
 
14.5%
23039
 
4.0%
3783
 
1.0%
4309
 
0.4%
598
 
0.1%
645
 
0.1%
728
 
< 0.1%
813
 
< 0.1%
97
 
< 0.1%
Other values (11)27
 
< 0.1%
ValueCountFrequency (%)
059920
79.7%
110893
 
14.5%
23039
 
4.0%
3783
 
1.0%
4309
 
0.4%
598
 
0.1%
645
 
0.1%
728
 
< 0.1%
813
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
181
 
< 0.1%
172
 
< 0.1%
161
 
< 0.1%
153
< 0.1%
144
< 0.1%
135
< 0.1%
122
 
< 0.1%
112
 
< 0.1%

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
No Deposit
74947 
Refundable
 
126
Non Refund
 
93

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters751660
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit74947
99.7%
Refundable126
 
0.2%
Non Refund93
 
0.1%

Length

2023-02-04T21:49:48.241565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:48.400949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no74947
49.9%
deposit74947
49.9%
refundable126
 
0.1%
non93
 
0.1%
refund93
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o149987
20.0%
e75292
10.0%
N75040
10.0%
75040
10.0%
s74947
10.0%
i74947
10.0%
t74947
10.0%
p74947
10.0%
D74947
10.0%
n312
 
< 0.1%
Other values (7)1254
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter526414
70.0%
Uppercase Letter150206
 
20.0%
Space Separator75040
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o149987
28.5%
e75292
14.3%
s74947
14.2%
i74947
14.2%
t74947
14.2%
p74947
14.2%
n312
 
0.1%
f219
 
< 0.1%
u219
 
< 0.1%
d219
 
< 0.1%
Other values (3)378
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N75040
50.0%
D74947
49.9%
R219
 
0.1%
Space Separator
ValueCountFrequency (%)
75040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin676620
90.0%
Common75040
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o149987
22.2%
e75292
11.1%
N75040
11.1%
s74947
11.1%
i74947
11.1%
t74947
11.1%
p74947
11.1%
D74947
11.1%
n312
 
< 0.1%
R219
 
< 0.1%
Other values (6)1035
 
0.2%
Common
ValueCountFrequency (%)
75040
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII751660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o149987
20.0%
e75292
10.0%
N75040
10.0%
75040
10.0%
s74947
10.0%
i74947
10.0%
t74947
10.0%
p74947
10.0%
D74947
10.0%
n312
 
< 0.1%
Other values (7)1254
 
0.2%

agent
Real number (ℝ≥0)

MISSING

Distinct314
Distinct (%)0.5%
Missing12310
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean94.0557942
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:48.576655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median14
Q3240
95-th percentile273
Maximum535
Range534
Interquartile range (IQR)231

Descriptive statistics

Standard deviation113.947162
Coefficient of variation (CV)1.211484768
Kurtosis-0.2374568407
Mean94.0557942
Median Absolute Deviation (MAD)11
Skewness0.9779323318
Sum5911971
Variance12983.95573
MonotonicityNot monotonic
2023-02-04T21:49:48.783041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
918697
24.9%
2408438
11.2%
73065
 
4.1%
142988
 
4.0%
2502357
 
3.1%
62265
 
3.0%
11911
 
2.5%
281556
 
2.1%
2411485
 
2.0%
81087
 
1.4%
Other values (304)19007
25.3%
(Missing)12310
16.4%
ValueCountFrequency (%)
11911
 
2.5%
2128
 
0.2%
3565
 
0.8%
416
 
< 0.1%
5181
 
0.2%
62265
 
3.0%
73065
 
4.1%
81087
 
1.4%
918697
24.9%
10196
 
0.3%
ValueCountFrequency (%)
5353
 
< 0.1%
53122
< 0.1%
52735
< 0.1%
5269
 
< 0.1%
5102
 
< 0.1%
5098
 
< 0.1%
5086
 
< 0.1%
50224
< 0.1%
4971
 
< 0.1%
4957
 
< 0.1%

company
Real number (ℝ≥0)

MISSING

Distinct331
Distinct (%)5.9%
Missing69560
Missing (%)92.5%
Infinite0
Infinite (%)0.0%
Mean190.5192651
Minimum6
Maximum541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:48.984565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile40
Q151
median183
Q3270
95-th percentile437
Maximum541
Range535
Interquartile range (IQR)219

Descriptive statistics

Standard deviation132.3492861
Coefficient of variation (CV)0.6946766566
Kurtosis-0.4896916298
Mean190.5192651
Median Absolute Deviation (MAD)107
Skewness0.5873683742
Sum1068051
Variance17516.33353
MonotonicityNot monotonic
2023-02-04T21:49:49.191918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40850
 
1.1%
223665
 
0.9%
45222
 
0.3%
153167
 
0.2%
219132
 
0.2%
174128
 
0.2%
154128
 
0.2%
281121
 
0.2%
233103
 
0.1%
405101
 
0.1%
Other values (321)2989
 
4.0%
(Missing)69560
92.5%
ValueCountFrequency (%)
61
 
< 0.1%
81
 
< 0.1%
934
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
1214
 
< 0.1%
148
 
< 0.1%
165
 
< 0.1%
181
 
< 0.1%
2039
0.1%
ValueCountFrequency (%)
5411
 
< 0.1%
5392
 
< 0.1%
5342
 
< 0.1%
5304
 
< 0.1%
5282
 
< 0.1%
52515
< 0.1%
52319
< 0.1%
5216
 
< 0.1%
5201
 
< 0.1%
5182
 
< 0.1%

days_in_waiting_list
Real number (ℝ≥0)

ZEROS

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.589867759
Minimum0
Maximum379
Zeros73827
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:49.402471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum379
Range379
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.78487546
Coefficient of variation (CV)9.299437246
Kurtosis192.7357548
Mean1.589867759
Median Absolute Deviation (MAD)0
Skewness12.59756589
Sum119504
Variance218.5925423
MonotonicityNot monotonic
2023-02-04T21:49:49.606509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073827
98.2%
58164
 
0.2%
8776
 
0.1%
12253
 
0.1%
6351
 
0.1%
3847
 
0.1%
17639
 
0.1%
7737
 
< 0.1%
22336
 
< 0.1%
6535
 
< 0.1%
Other values (88)801
 
1.1%
ValueCountFrequency (%)
073827
98.2%
19
 
< 0.1%
24
 
< 0.1%
417
 
< 0.1%
53
 
< 0.1%
616
 
< 0.1%
71
 
< 0.1%
83
 
< 0.1%
92
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
3796
 
< 0.1%
33014
 
< 0.1%
25910
 
< 0.1%
23629
< 0.1%
2244
 
< 0.1%
22336
< 0.1%
2158
 
< 0.1%
20710
 
< 0.1%
18722
< 0.1%
1852
 
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
Transient
53099 
Transient-Party
18735 
Contract
 
2814
Group
 
518

Length

Max length15
Median length9
Mean length10.43048719
Min length5

Characters and Unicode

Total characters784018
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient53099
70.6%
Transient-Party18735
 
24.9%
Contract2814
 
3.7%
Group518
 
0.7%

Length

2023-02-04T21:49:50.928651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:51.092463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
transient53099
70.6%
transient-party18735
 
24.9%
contract2814
 
3.7%
group518
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n146482
18.7%
t96197
12.3%
r93901
12.0%
a93383
11.9%
T71834
9.2%
s71834
9.2%
i71834
9.2%
e71834
9.2%
y18735
 
2.4%
-18735
 
2.4%
Other values (7)29249
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter671382
85.6%
Uppercase Letter93901
 
12.0%
Dash Punctuation18735
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n146482
21.8%
t96197
14.3%
r93901
14.0%
a93383
13.9%
s71834
10.7%
i71834
10.7%
e71834
10.7%
y18735
 
2.8%
o3332
 
0.5%
c2814
 
0.4%
Other values (2)1036
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
T71834
76.5%
P18735
 
20.0%
C2814
 
3.0%
G518
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
-18735
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin765283
97.6%
Common18735
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n146482
19.1%
t96197
12.6%
r93901
12.3%
a93383
12.2%
T71834
9.4%
s71834
9.4%
i71834
9.4%
e71834
9.4%
y18735
 
2.4%
P18735
 
2.4%
Other values (6)10514
 
1.4%
Common
ValueCountFrequency (%)
-18735
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII784018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n146482
18.7%
t96197
12.3%
r93901
12.0%
a93383
11.9%
T71834
9.2%
s71834
9.2%
i71834
9.2%
e71834
9.2%
y18735
 
2.4%
-18735
 
2.4%
Other values (7)29249
 
3.7%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size587.4 KiB
0
67750 
1
7383 
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters75166
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
067750
90.1%
17383
 
9.8%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Length

2023-02-04T21:49:51.239561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-04T21:49:51.401765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
067750
90.1%
17383
 
9.8%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
067750
90.1%
17383
 
9.8%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75166
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
067750
90.1%
17383
 
9.8%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common75166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
067750
90.1%
17383
 
9.8%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII75166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
067750
90.1%
17383
 
9.8%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

total_of_special_requests
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7140595482
Minimum0
Maximum5
Zeros36762
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:51.537958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.833886853
Coefficient of variation (CV)1.167811361
Kurtosis0.8969141785
Mean0.7140595482
Median Absolute Deviation (MAD)1
Skewness1.077975244
Sum53673
Variance0.6953672836
MonotonicityNot monotonic
2023-02-04T21:49:51.676352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
036762
48.9%
125908
34.5%
210103
 
13.4%
32051
 
2.7%
4304
 
0.4%
538
 
0.1%
ValueCountFrequency (%)
036762
48.9%
125908
34.5%
210103
 
13.4%
32051
 
2.7%
4304
 
0.4%
538
 
0.1%
ValueCountFrequency (%)
538
 
0.1%
4304
 
0.4%
32051
 
2.7%
210103
 
13.4%
125908
34.5%
036762
48.9%

stays_total
Real number (ℝ≥0)

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.393023441
Minimum0
Maximum69
Zeros680
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size587.4 KiB
2023-02-04T21:49:51.870397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum69
Range69
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.577670726
Coefficient of variation (CV)0.7596972936
Kurtosis30.80112497
Mean3.393023441
Median Absolute Deviation (MAD)1
Skewness3.19584567
Sum255040
Variance6.64438637
MonotonicityNot monotonic
2023-02-04T21:49:52.129663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
115749
21.0%
315725
20.9%
215480
20.6%
411025
14.7%
75686
 
7.6%
55121
 
6.8%
62322
 
3.1%
10783
 
1.0%
8720
 
1.0%
0680
 
0.9%
Other values (33)1875
 
2.5%
ValueCountFrequency (%)
0680
 
0.9%
115749
21.0%
215480
20.6%
315725
20.9%
411025
14.7%
55121
 
6.8%
62322
 
3.1%
75686
 
7.6%
8720
 
1.0%
9528
 
0.7%
ValueCountFrequency (%)
691
 
< 0.1%
601
 
< 0.1%
571
 
< 0.1%
561
 
< 0.1%
491
 
< 0.1%
481
 
< 0.1%
461
 
< 0.1%
451
 
< 0.1%
431
 
< 0.1%
423
< 0.1%

Interactions

2023-02-04T21:49:33.700717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:28.484392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:33.417613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:37.846160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:41.622178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:45.576698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:51.022441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:55.478127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:59.525337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:04.146639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:14.475695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:26.332249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:30.122846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:33.995837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:28.748803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:33.684959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:38.044689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:41.830491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:45.811107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:51.340872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:55.704320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:59.730750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:05.062258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:15.085583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:26.549455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:30.386447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:34.231707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:29.071978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:33.904011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:38.271568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:42.048134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:46.044356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:51.665786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:55.931751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:59.938303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:05.955909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:15.732583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:26.763152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:30.598226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:34.477391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:29.368309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:34.126691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:38.486563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:42.266697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:46.280318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:52.029495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:56.159523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:00.161407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:06.704383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:16.377811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:26.974155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:30.792360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:34.703481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:29.661376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:34.369304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:38.695622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:42.484057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:46.882969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:52.297418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:56.387016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:00.375745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:07.335385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:17.003250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:27.173317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:30.977747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:34.933527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:29.964115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:34.652467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:38.934448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:42.731007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:47.104817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:52.784139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:56.650515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:00.603768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:07.959408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:17.742282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:27.376454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:31.170652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:35.138091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:30.186146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:34.950485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:39.159266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:42.953405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:47.348419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:53.003419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:56.877660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:00.854938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:08.577819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:18.688410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:27.591350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:31.383723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:35.339136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:30.431727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:35.174207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:39.391520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:43.183758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:47.576064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:53.234121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:57.126363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:01.082386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:09.205642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:19.576662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:27.832092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:31.597927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:35.545140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:30.671742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:35.461320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:39.624446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:43.424626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:47.825943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:53.477386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:57.382747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:01.328500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:09.841017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:20.350995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:28.056704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:31.818369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:36.192352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:31.484980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:36.467568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:40.291172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:44.150391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:48.780541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:54.153094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:58.110596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:02.546408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:10.924030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:21.462230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:28.751614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:32.450688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:36.847551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:32.507708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:37.152316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:40.942369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:44.851431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:49.823626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:54.821330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:58.834772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:03.264655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:12.641129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:22.613760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:29.426973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:33.096822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:37.047033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:32.825518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:37.382530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:41.149005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:45.120754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:50.161111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:55.050162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:59.062081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:03.544299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:13.245843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:23.665194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:29.633462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:33.288726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:37.238158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:33.106458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:37.614594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:41.366384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:45.355221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:50.635224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:55.245768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:48:59.297755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:03.829499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:13.855896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:25.699675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:29.843307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-04T21:49:33.493610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Missing values

2023-02-04T21:49:37.720646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-04T21:49:39.135864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-04T21:49:40.118033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-02-04T21:49:40.468481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexhotellead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typerequired_car_parking_spacestotal_of_special_requestsstays_total
00Resort Hotel3422015727120.00BBPRTDirectDirect000CC3.0No Depositnannan0Transient000
11Resort Hotel7372015727120.00BBPRTDirectDirect000CC4.0No Depositnannan0Transient000
22Resort Hotel72015727110.00BBGBRDirectDirect000AC0.0No Depositnannan0Transient001
33Resort Hotel132015727110.00BBGBRCorporateCorporate000AA0.0No Deposit304.0nan0Transient001
44Resort Hotel142015727120.00BBGBROnline TATA/TO000AA0.0No Deposit240.0nan0Transient012
55Resort Hotel142015727120.00BBGBROnline TATA/TO000AA0.0No Deposit240.0nan0Transient012
66Resort Hotel02015727120.00BBPRTDirectDirect000CC0.0No Depositnannan0Transient002
77Resort Hotel92015727120.00FBPRTDirectDirect000CC0.0No Deposit303.0nan0Transient012
811Resort Hotel352015727120.00HBPRTOnline TATA/TO000DD0.0No Deposit240.0nan0Transient004
912Resort Hotel682015727120.00BBUSAOnline TATA/TO000DE0.0No Deposit240.0nan0Transient034

Last rows

df_indexhotellead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typerequired_car_parking_spacestotal_of_special_requestsstays_total
75156119380City Hotel4420178353120.00SCDEUOnline TATA/TO000AA0.0No Deposit9.0nan0Transient014
75157119381City Hotel18820178353120.00BBDEUDirectDirect000AA0.0No Deposit14.0nan0Transient005
75158119382City Hotel13520178353030.00BBJPNOnline TATA/TO000GG0.0No Deposit7.0nan0Transient006
75159119383City Hotel16420178353120.00BBDEUOffline TA/TOTA/TO000AA0.0No Deposit42.0nan0Transient006
75160119384City Hotel2120178353020.00BBBELOffline TA/TOTA/TO000AA0.0No Deposit394.0nan0Transient027
75161119385City Hotel2320178353020.00BBBELOffline TA/TOTA/TO000AA0.0No Deposit394.0nan0Transient007
75162119386City Hotel10220178353130.00BBFRAOnline TATA/TO000EE0.0No Deposit9.0nan0Transient027
75163119387City Hotel3420178353120.00BBDEUOnline TATA/TO000DD0.0No Deposit9.0nan0Transient047
75164119388City Hotel10920178353120.00BBGBROnline TATA/TO000AA0.0No Deposit89.0nan0Transient007
75165119389City Hotel20520178352920.00HBDEUOnline TATA/TO000AA0.0No Deposit9.0nan0Transient029